Bio-Inspired Multiobjective Optimization for Designing Content Distribution Networks

Autores
Goñi, Gerardo; Nesmachnow, Sergio; Rossit, Diego Gabriel; Moreno Bernal, Pedro; Tchernykh, Andrei
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This article studies the effective design of content distribution networks over cloud computing platforms. This problem is relevant nowadays to provide fast and reliable access to content on the internet. A bio-inspired evolutionary multiobjective optimization approach is applied as a viable alternative to solve realistic problem instances where exact optimization methods are not applicable. Ad hoc representation and search operators are applied to optimize relevant metrics from the point of view of both system administrators and users. In the evaluation of problem instances built using real data, the evolutionary multiobjective optimization approach was able to compute more accurate solutions in terms of cost and quality of service when compared to the exact resolution method. The obtained results represent an improvement over greedy heuristics from 47.6% to 93.3% in terms of cost while maintaining competitive quality of service. In addition, the computed solutions had different tradeoffs between the problem objectives. This can provide different options for content distribution network design, allowing for a fast configuration that fulfills specific quality of service demands.
Fil: Goñi, Gerardo. Universidad de la República; Uruguay
Fil: Nesmachnow, Sergio. Universidad de la República; Uruguay
Fil: Rossit, Diego Gabriel. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina
Fil: Moreno Bernal, Pedro. Universidad Autónoma del Estado de Morelos.; México
Fil: Tchernykh, Andrei. Consejo Nacional de Ciencia y Tecnología de México. Centro de Investigación Científica y de Educación Superior de Ensenada Baja California; México
Materia
CONTENT DISTRIBUTION NETWORKS
EVOLUTIONARY ALGORITHMS
OPTIMIZATION
CLOUD COMPUTING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/260892
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Bio-Inspired Multiobjective Optimization for Designing Content Distribution NetworksGoñi, GerardoNesmachnow, SergioRossit, Diego GabrielMoreno Bernal, PedroTchernykh, AndreiCONTENT DISTRIBUTION NETWORKSEVOLUTIONARY ALGORITHMSOPTIMIZATIONCLOUD COMPUTINGhttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2This article studies the effective design of content distribution networks over cloud computing platforms. This problem is relevant nowadays to provide fast and reliable access to content on the internet. A bio-inspired evolutionary multiobjective optimization approach is applied as a viable alternative to solve realistic problem instances where exact optimization methods are not applicable. Ad hoc representation and search operators are applied to optimize relevant metrics from the point of view of both system administrators and users. In the evaluation of problem instances built using real data, the evolutionary multiobjective optimization approach was able to compute more accurate solutions in terms of cost and quality of service when compared to the exact resolution method. The obtained results represent an improvement over greedy heuristics from 47.6% to 93.3% in terms of cost while maintaining competitive quality of service. In addition, the computed solutions had different tradeoffs between the problem objectives. This can provide different options for content distribution network design, allowing for a fast configuration that fulfills specific quality of service demands.Fil: Goñi, Gerardo. Universidad de la República; UruguayFil: Nesmachnow, Sergio. Universidad de la República; UruguayFil: Rossit, Diego Gabriel. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaFil: Moreno Bernal, Pedro. Universidad Autónoma del Estado de Morelos.; MéxicoFil: Tchernykh, Andrei. Consejo Nacional de Ciencia y Tecnología de México. Centro de Investigación Científica y de Educación Superior de Ensenada Baja California; MéxicoMultidisciplinary Digital Publishing Institute2025-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/260892Goñi, Gerardo; Nesmachnow, Sergio; Rossit, Diego Gabriel; Moreno Bernal, Pedro; Tchernykh, Andrei; Bio-Inspired Multiobjective Optimization for Designing Content Distribution Networks; Multidisciplinary Digital Publishing Institute; Mathematical and Computational Applications; 30; 2; 4-2025; 1-351300-686X2297-8747CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2297-8747/30/2/45info:eu-repo/semantics/altIdentifier/doi/10.3390/mca30020045info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-12-23T14:07:50Zoai:ri.conicet.gov.ar:11336/260892instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-12-23 14:07:50.387CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Bio-Inspired Multiobjective Optimization for Designing Content Distribution Networks
title Bio-Inspired Multiobjective Optimization for Designing Content Distribution Networks
spellingShingle Bio-Inspired Multiobjective Optimization for Designing Content Distribution Networks
Goñi, Gerardo
CONTENT DISTRIBUTION NETWORKS
EVOLUTIONARY ALGORITHMS
OPTIMIZATION
CLOUD COMPUTING
title_short Bio-Inspired Multiobjective Optimization for Designing Content Distribution Networks
title_full Bio-Inspired Multiobjective Optimization for Designing Content Distribution Networks
title_fullStr Bio-Inspired Multiobjective Optimization for Designing Content Distribution Networks
title_full_unstemmed Bio-Inspired Multiobjective Optimization for Designing Content Distribution Networks
title_sort Bio-Inspired Multiobjective Optimization for Designing Content Distribution Networks
dc.creator.none.fl_str_mv Goñi, Gerardo
Nesmachnow, Sergio
Rossit, Diego Gabriel
Moreno Bernal, Pedro
Tchernykh, Andrei
author Goñi, Gerardo
author_facet Goñi, Gerardo
Nesmachnow, Sergio
Rossit, Diego Gabriel
Moreno Bernal, Pedro
Tchernykh, Andrei
author_role author
author2 Nesmachnow, Sergio
Rossit, Diego Gabriel
Moreno Bernal, Pedro
Tchernykh, Andrei
author2_role author
author
author
author
dc.subject.none.fl_str_mv CONTENT DISTRIBUTION NETWORKS
EVOLUTIONARY ALGORITHMS
OPTIMIZATION
CLOUD COMPUTING
topic CONTENT DISTRIBUTION NETWORKS
EVOLUTIONARY ALGORITHMS
OPTIMIZATION
CLOUD COMPUTING
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This article studies the effective design of content distribution networks over cloud computing platforms. This problem is relevant nowadays to provide fast and reliable access to content on the internet. A bio-inspired evolutionary multiobjective optimization approach is applied as a viable alternative to solve realistic problem instances where exact optimization methods are not applicable. Ad hoc representation and search operators are applied to optimize relevant metrics from the point of view of both system administrators and users. In the evaluation of problem instances built using real data, the evolutionary multiobjective optimization approach was able to compute more accurate solutions in terms of cost and quality of service when compared to the exact resolution method. The obtained results represent an improvement over greedy heuristics from 47.6% to 93.3% in terms of cost while maintaining competitive quality of service. In addition, the computed solutions had different tradeoffs between the problem objectives. This can provide different options for content distribution network design, allowing for a fast configuration that fulfills specific quality of service demands.
Fil: Goñi, Gerardo. Universidad de la República; Uruguay
Fil: Nesmachnow, Sergio. Universidad de la República; Uruguay
Fil: Rossit, Diego Gabriel. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina
Fil: Moreno Bernal, Pedro. Universidad Autónoma del Estado de Morelos.; México
Fil: Tchernykh, Andrei. Consejo Nacional de Ciencia y Tecnología de México. Centro de Investigación Científica y de Educación Superior de Ensenada Baja California; México
description This article studies the effective design of content distribution networks over cloud computing platforms. This problem is relevant nowadays to provide fast and reliable access to content on the internet. A bio-inspired evolutionary multiobjective optimization approach is applied as a viable alternative to solve realistic problem instances where exact optimization methods are not applicable. Ad hoc representation and search operators are applied to optimize relevant metrics from the point of view of both system administrators and users. In the evaluation of problem instances built using real data, the evolutionary multiobjective optimization approach was able to compute more accurate solutions in terms of cost and quality of service when compared to the exact resolution method. The obtained results represent an improvement over greedy heuristics from 47.6% to 93.3% in terms of cost while maintaining competitive quality of service. In addition, the computed solutions had different tradeoffs between the problem objectives. This can provide different options for content distribution network design, allowing for a fast configuration that fulfills specific quality of service demands.
publishDate 2025
dc.date.none.fl_str_mv 2025-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/260892
Goñi, Gerardo; Nesmachnow, Sergio; Rossit, Diego Gabriel; Moreno Bernal, Pedro; Tchernykh, Andrei; Bio-Inspired Multiobjective Optimization for Designing Content Distribution Networks; Multidisciplinary Digital Publishing Institute; Mathematical and Computational Applications; 30; 2; 4-2025; 1-35
1300-686X
2297-8747
CONICET Digital
CONICET
url http://hdl.handle.net/11336/260892
identifier_str_mv Goñi, Gerardo; Nesmachnow, Sergio; Rossit, Diego Gabriel; Moreno Bernal, Pedro; Tchernykh, Andrei; Bio-Inspired Multiobjective Optimization for Designing Content Distribution Networks; Multidisciplinary Digital Publishing Institute; Mathematical and Computational Applications; 30; 2; 4-2025; 1-35
1300-686X
2297-8747
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2297-8747/30/2/45
info:eu-repo/semantics/altIdentifier/doi/10.3390/mca30020045
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.952241

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